论文标题
使用有机液体闪烁体信号的卷积神经网络脉冲形状歧视
Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals
论文作者
论文摘要
开发了卷积神经网络（CNN）结构，以提高加多林载体有机液体闪烁探测器的脉冲形状歧视（PSD）功率，以减少NEOS-II数据的逆β衰减候选事件中的快速中子背景。事件的功率谱是使用时域原始波形的快速傅立叶变换构建的，并放入CNN中。在使用低能$β$和$α$事件训练后，CNN评估了早期数据集。与现有的常规PSD方法相比，CNN方法的结果超过1-10 MEV可见能量范围的信噪比平均超过20％，并且在低能区域中的改善更高。
A convolutional neural network (CNN) architecture is developed to improve the pulse shape discrimination (PSD) power of the gadolinium-loaded organic liquid scintillation detector to reduce the fast neutron background in the inverse beta decay candidate events of the NEOS-II data. A power spectrum of an event is constructed using a fast Fourier transform of the time domain raw waveforms and put into CNN. An early data set is evaluated by CNN after it is trained using low energy $β$ and $α$ events. The signal-to-background ratio averaged over 1-10 MeV visible energy range is enhanced by more than 20% in the result of the CNN method compared to that of an existing conventional PSD method, and the improvement is even higher in the low energy region.
